Prediction of phthalate in dust in children's bedroom based on gradient boosting regression tree.

This study developed a prediction method to determine the distribution of phthalate esters (PAEs) in indoor dust. A gradient boosting decision tree model (GBRT) was trained by using 267 samples in Shanghai, including PAEs concentrations in indoor dust and data obtained from continuous monitoring, as well as the survey of indoor environment. Environmental exposure, residents' lifestyle, and building characteristics data were collected from 8 cities in China. Based on this, the well-trained GBRT model accurately predicted PAEs concentrations, with goodness of fit (R ² ) > 0.94, mean absolute error (MAE) approaching 0, and mean squared error (MSE) approaching 0. This study identified key relationships between input parameters and PAEs concentrations. The average increment of PAEs concentration was greater than 50 % when using more than 2 electronic devices in bedroom. Diisobutyl phthalate (DiBP) concentration increased by approximately 200 % when cleaning frequency was less than once every fortnight. Bis (2-ethylhexyl) phthalate (DEHP) concentration increased by over 43 % when dampness-related exposure indicators exceeding 3, and by up to 74 % with extensive usage of polyvinyl chloride (PVC) floorings. Furthermore, the study found higher PAEs concentrations in southern China compared to northern cities. • Apply multidisciplinary method to study the factors affecting the differences in indoor phthalate distribution. • Set up a gradient boosted regression trees model to predict the concentration of phthalate and evaluate the result. • Predict and analyze the distribution of phthalate in eight typical cities in China, providing a data support for impacting assessment of human health. • Provide a theoretical support and scientific method for predicting concentration of phthalate in dust in children's bedroom, with a high popularization value. [ABSTRACT FROM AUTHOR]